• Transactions of the Korean Society of Mechanical Engineers A
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• v.22 no.1
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• pp.148-159
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• 1998

Spot welding palys a key role in increasing productivity and weight reduction of the final products. This paper proposes a systematic approach on the design of spot weld configuration, dealing with the requried number and location of spot weld joints under the given design parameters, such as the applied loads, lap area, and individual spot weld strength. The optimal design of a spot-welded joint is postulated as a state when the safety factors of all spot weld points (i) are evenly distributed and (ii) reach maximum value. A CAD program is developed to arrange the optimal location of each spot weld based on the derived objective function and constraints. The CAD tool integrates the optimization procedure with Finite Element Analysis (FEA) code through an interface. The interface automatically provides geometrical data and mesh configuration for different spot weld locations to FEA model. It also extracts the transmitted load of each spot weld from the FEA code, and allows the optimization code predict an improved arrangement of spot weld locations. The feasibility of the developed approach is demonstrated by the selected examples.

• Journal of Korean Society of Steel Construction
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• v.17 no.6 s.79
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• pp.661-672
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• 2005

For the efficient stochastic optimization of steel structures for which a large number of analyses is required, artificial neural networks,which have emerged as a powerful tool that could have been used to replace time-consuming procedures in many scientific or engineering applications, are applied. They are utilized for the solution of the equilibrium equations resulting from the application of the finite element method in connection with the reanalysis type of problem, for which a large number of finite element analyses are required in this study. As such, the use of artificial neural networks to predict finite element analysis outputs simplifies and facilitates the performance of the stochastic optimal design of structural systems where a trained neural network is used to replace the structural reanalysis phase. Moreover, to improve efficiency of used artificial neural networks, genetic algorithm is utilized. The stochastic optimizer used in this study is an algorithm based on the evolution theory. The efficiency of the proposed procedure is examined in problems with both volume (weight) functions and real-world cost functions

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.2
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• pp.236-247
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• 1992

Optimization has been developed to minimize the cost function while satisfying constraints. Nonlinear Programming method is used as a tool for the optimization. Usually, cost and constraint function calculations are required in the engineering applications, but those calculations are extremely expensive. Especially, the function and sensitivity analyses cause a bottleneck in structural optimization which utilizes the Finite Element Method. Also, when the functions are quite noisy, the informations do not carry out proper role in the optimization process. An algorithm called "Second-order Approximation Method" has been proposed to overcome the difficulties recently. The cost and constraint functions are approximated by the second-order Taylor series expansion on a nominal points in the algorithm. An optimal design problem is defined with the approximated functions and the approximated problem is solved by a nonlinear programming numerical algorithm. The solution is included in a candidate point set which is evaluated for a new nominal point. Since the functions are approximated only by the function values, sensitivity informations are not needed. One-dimensional line search is unnecessary due to the fact that the nonlinear algorithm handles the approximated functions. In this research, the method is analyzed and the performance is evaluated. Several mathematical problems are created and some standard engineering problems are selected for the evaluation. Through numerical results, applicabilities of the algorithm to large scale and complex problems are presented.presented.

• Computational Structural Engineering
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• v.5 no.1
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• pp.133-142
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• 1992

This paper presents a method to apply the linear goal programming, which has rarely been used to the structural opimization problem due to its unique formulation, to large scale nonlinear structural optimization. The method can be used as a multicriteria optimization tool since goal programming removes the difficulty in defining an objective function and constraints. The method uses the finite element analysis, linear goal programming techniques and successive linearization to obtain the solution for the nonlinear goal optimization problems. The general formulation of the structural optimization problem into a nonlinear goal programming form is presented. The successive linearization method for the nonlinear goal optimization problem is discussed. To demonstrate the validity of the method, as a design tool, the minimum weight structural optimization problems with stress constraints are solved for the cases of 10, 25 and 200 trusses and compared with the results of the other works.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.543-547
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• 2001

CAE has been settled down to an indispensable tool for the simulation of a mechanical system according to the development of computer-aided analysis rapidly. Particularly finite element programs have advanced to the one of most valuable things in the filed of CAE due to the remarkable progress in the implementation. But since this analysis tool mostly provides the result of the analysis, it cannot satisfy designers who are seeking for information to improve their designs. Therefore, design sensitivity analysis or optimization module has been incorporated into commercial FEA programs to satisfy the desire of designers since 1990s. Design sensitivity analysis is to compute the rate of change of response with respected to design variable. Design sensitivity analysis is classfied into static design sensitivity analysis, Eigenvalue design sensitivity analysis and dynamic design sensitivity analysis. In this research, it will be presented to nonlinear static design sensitivity analysis formulation and nonlinear static design sensitivity analysis external module based ANSYS have been developed and illustrated an example to verify the developed module.

• Journal of Magnetics
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• v.21 no.4
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• pp.544-553
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• 2016

Torque ripple minimization without decrease in average torque is a vital attribute in the design of Synchronous Reluctance (SynRel) motor. As the design of SynRel motor is an arduous task, which encompasses many design variables, this work first analyses the significance of the effect of varying the geometrical parameters on average torque and torque ripple and then proposes an extensive optimization procedure to obtain configurations with improved average torque and minimized torque ripple. A hardware prototype is fabricated and tested. The Finite Element Analysis (FEA) software tool used for validating the test results is MagNet 7.6.0.8. Multi Objective Particle Swarm Optimization (MOPSO) is used to determine the various designs meeting the requirements of reduced torque ripple and improved torque performance. The results indicate the efficacy of the proposed methodology and substantiate the utilization of MOPSO as a significant tool for solving design problems related to SynRel motor.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.138-143
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• 2005

The important problem in the high speed spindles is to reduce and minimize the thermal effect by the motor and ball bearings. Thermal characteristics according to the bearing preload and hollow shaft cooling are studied for the spindle with the oil mist lubrication and high frequency motor. Temperature distribution and thermal deformation according to the spindle speed, preload and flow rate are measured by thermocouple and gap sensor. Temperature distribution and thermal deformation are analyzed by using the finite element method. The results of analysis are compared with the measured data. This paper show that the suitable preload and hollow shaft cooling are very effective to minimize the thermal effect by the motor and ball bearings. This study indicates that temperature distribution and thermal deformation of the high speed spindle system can be estimated reasonably by using the three dimensional model through the finite element method and supports thermal optimization and more effective cooling method.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.5
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• pp.102-106
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• 2015

In this paper, we study the optimal design for improved rotation latch system performance. The factors affecting the Torque generated in the armature were chosen as design variables. Utilizing the vertical matrix, the orthogonal array table was created to predict the results through minimal analysis. To confirm the Torque generation amount, by utilizing the commercial electromagnetic analysis software MAXWELL, finite element analysis was performed. The approximation method and experimental design through the commercial PIDO tool PIAnO for optimal design and calculations were utilized to perform experiments using an optimization method with evolutionary algorithms. Using the approximation model, design factors were determined that can maximize the torque generated in the armature, and the simulation was performed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.13 no.5
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• pp.50-56
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• 2014

A technique based on the finite element method (FEM) is used in the simulation of metal cutting process. This offers the advantages of the prediction of the cutting force, the stresses, the temperature, the tool wear, and optimization of the cutting condition, the tool shape and the residual stress of the surface. However, the accuracy and reliability of prediction depend on the flow stress of the workpiece. There are various models which describe the relationship between the flow stress and the strain. The Johnson-Cook model is a well-known material model capable of doing this. Low-alloy steel is developed for a dry storage container for used nuclear fuel. Related to this, a process analysis of the plastic machining capability is necessary. For a plastic processing analysis of machining or forging, there are five parameters that must be input into the Johnson-Cook model in this paper. These are (1) the determination of the strain-hardening modulus and the strain hardening exponent through a room-temperature tensile test, (2) the determination of the thermal softening exponent through a high-temperature tensile test, (3) the determination of the cutting forces through an orthogonal cutting test at various cutting speeds, (4) the determination of the strain-rate hardening modulus comparing the orthogonal cutting test results with FEM results. (5) Finally, to validate the Johnson-Cook material parameters, a comparison of the room-temperature tensile test result with a quasi-static simulation using LS-Dyna is necessary.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.3 s.174
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• pp.645-652
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• 2000

A method for shape design sensitivity analysis is proposed utilizing commercial software ANSYS for thermal conduction problems. While the sensitivity formula is derived analytically by introduing adjoint variable concept, sensitivity calculation in practice as well as the primal and adjoint solution of thermal conduction is performed using the ANSYS very easily. Since the formula always takes boundary integral form, sensitivity evaluation in ANSYS requires a little more addition of post-processing routine which involves evaluation of boundary variable from the obtained solution. Though the BEM has been used as a better tool for this purpose, the present study shows it can also be calculated using any kind of analysis code such as ANSYS since the formula is based on analytic nature. Therefore the present study provides a new and efficient way of optimization which was not possible before using commercial software. The usefulness of the method is illustrated via a weight minimization problem of thermal diffuser.